where \(x \in \mathbb {R}^{m}\)  is the input feature, \(f_{i}: \mathbb {R} \rightarrow \mathbb {R}\)  is the MLP corresponding to the ith feature, \(b \in \mathbb {R}\)  is the output bias, and \(m\)  is the number of features.
As shown in Equation REF , NAM is an extension of the generalized additive models (GAM) [1]}.
The outputs of all feature networks in NAM are scalars, but the outputs should be vectors to effectively model the feature interactions.
Therefore, we transform NAM as follows:
\(\mathcal {F} (x) = \sum _{i=1}^{m} f_{i} \left( x_{i} \right),\) 
